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An on-premises AI coding agent needs more than a capable computer: plan separately for the agent application and its development sandbox, and for the model-serving machine if inference also runs locally. The agent host can have modest baseline requirements, but inference capacity depends on the selected model, quantization, context length, latency target, and number of simultaneous requests. For one documented example, OpenHands recommends at least 24 GB of GPU VRAM—or at least 64 GB of unified memory on Apple Silicon—for quantized Qwen3.6-35B-A3B.
Separate the agent host from the model server
The agent application coordinates coding tasks and tools; a sandbox provides a controlled place to access repositories and run commands. The model server performs inference. These components can share a machine, but sizing the application host does not tell you whether that machine can serve your chosen model.
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OpenHands’ local setup documentation supports Linux, macOS with Docker Desktop, and Windows with WSL and Docker Desktop. It recommends a modern processor and at least 4 GB of RAM for the application setup, and describes mounting local code into the sandbox. That 4 GB recommendation is for the application—not a model server, builds, tests, browser or tool processes, or multiple sandboxes. OpenHands local setup
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →There is no universal CPU, memory, disk, or isolation specification for every coding-agent product in the cited guidance. Size the workspace and sandbox for your repository, build and test tools, parallel jobs, and security policy. Decide which repositories and commands the agent may access, and keep that decision separate from the question of model capacity.
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Size inference for a specific model and context
OpenHands’ local LLM guide recommends Qwen3.6-35B-A3B, an open-weight mixture-of-experts model aimed at agentic coding. For quantized variants, its recommendation note dated May 21, 2026, calls for a recent GPU with at least 24 GB of VRAM or Apple Silicon with at least 64 GB of unified memory. These are model-specific starting points, not universal minimums for local coding agents or guarantees of a particular speed. OpenHands local LLM setup
The same guide recommends a context length of at least 22,000 for lower-VRAM systems, or 32,768 for better performance in the described configuration, and says to enable Flash Attention. Treat these as settings for that guide’s setup, not promises that every system can sustain those contexts at a given latency. Context, runtime overhead, and concurrent requests all affect how much capacity remains available.
Do not merge unlike model examples into one hardware rule. In a March 31, 2025 announcement, OpenHands said its separate OpenHands LM 32B could run locally on hardware such as a single RTX 3090. That historical example concerns a different model and does not establish the memory behavior of Qwen3.6-35B-A3B. OpenHands LM 32B announcement
Check serving software against your operating system and accelerator
The inference runtime can rule out a configuration even when the machine has enough memory. For example, vLLM’s stable GPU installation guide specifies Linux and Python 3.10–3.13. It lists NVIDIA GPUs with compute capability 7.5 or newer, supported AMD GPU families subject to ROCm qualifications, and supported Intel data-center or Arc GPUs. Check the current guide for the precise hardware and platform qualifications before choosing a server. vLLM GPU installation guide
Apple Silicon is a separate implementation path in this context: the vLLM guide points users to a community-maintained vLLM-Metal plugin, rather than describing Apple Silicon as ordinary vLLM GPU support. If running vLLM in a container, account for host shared memory; the guide gives ipc=host or an explicit shared-memory allocation as options, particularly for tensor-parallel inference.
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Make the model endpoint reachable—and deliberately bounded
The agent must be able to reach the model server’s base URL. One specific pitfall documented by OpenHands is using LM Studio on a Linux host with its default listener at 127.0.0.1: in the described Docker arrangement, the OpenHands container cannot reach that host-local address. This is a configuration issue for that setup, not a rule that containers can never access host services. OpenHands local LLM setup
Configure and test the endpoint from the agent’s actual runtime environment. Decide how authentication and firewall rules limit access; the cited setup guidance does not establish a general production network design or recommend exposing an unauthenticated model API.
Plan a shared server around measured workload
The documented memory examples do not establish how many developers a server can serve or how quickly it will respond under load. Before sizing shared inference, specify the workload you need to support:
- Model, quantization, and target context length.
- Expected simultaneous generations and acceptable first-token and completion latency.
- Repository size, tool-call patterns, and build or test activity competing for resources.
- Whether users share one model process or receive isolated instances.
- Runtime, operating system, accelerator, drivers, and model-server interface compatibility.
- Whether the agent, sandbox, and inference service share a host or run across machines, and how the endpoint is secured.
Benchmark the exact deployment under representative conditions before committing to a multi-user capacity target. A model-loading threshold for one configuration is not a throughput guarantee. For physical expansion, also account for the number of accelerators, power, cooling, chassis constraints, maintainability, and vendor support; the cited guides do not quantify those requirements.
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